Gain insights into simplifying machine learning with PyCaret. Delve into regression, classification, clustering, and anomaly detection, and learn to deploy applications using Streamlit.

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PyCaret is an low-code, open-source, machine learning library for Python. It can be used in machine learning tasks such as data preparation and model deployment.

In this course, you will learn multiple topics related to machine learning. You will start with a brief introduction to the basic concepts of machine learning, and then continue with case studies of regression, classification, clustering, and anomaly detection based on the respective modules of the PyCaret library. Finally, we will focus on using the Streamlit library to develop and deploy machine learning applications.

By the end of this course, you will have the skills to deploy the robust PyCaret library for any of your machine learning projects.

Simplifying Machine Learning with PyCaret in Python

We’ll now perform EDA on our data. As mentioned earlier, EDA is a method that helps us understand the dataset properties by using descriptive statistics and visualization. It is an important part of every machine learning or data science project because it’s essential that we understand the data set before we utilize it.

# Histogram of numeric variables
The distribution of numeric variables can be visualized with a histogram that can be easily created with the `hist()` function.


# Histogram of numeric variables

numeric = ['age', 'bmi', 'children', 'charges']
data[numeric].hist(bins=20, figsize = (10,5))
plt.show()

As we can see in the output, some of the variables have right-skewed distributions that may cause problems with regression models, so we’ll have to deal with that later.
# Bar charts of categorical variables
Using bar charts is the standard way of plotting categorical variables. We can accomplish that easily by using the `value_counts()` and `plot()` functions.

Let’s learn how to perform exploratory data analysis for regression on our dataset and plot histograms, bar charts, and scatter plots.

Introduction to Machine Learning

Regression

Classification

Clustering

Anomaly Detection

Natural Language Processing

Deploying a Machine Learning Model

Conclusion

Appendix

Exploratory Data Analysis

Histogram of numeric variables